Oxygen Uptake Estimation during Cardiopulmonary Exercise Testing Using Temporal Fusion Networks

Accurate measurement of oxygen uptake ( \(\dot{\mathrm{V}}\mathrm{O}_{2}\) ) dynamics and maximal oxygen consumption ( \(\dot{\mathrm{V}}\mathrm{O}_{2}\max\) ), a vital marker of cardiorespiratory fitness and exercise capacity, requires specialized exercise physiology laboratories with costly equipment. This study develops a Temporal Fusion Network (TFN) approach utilizing easily accessible physiological parameters (heart rate, heart rate reserve, tidal volume, and breathing frequency), which can be measured with wearable sensors, anthropometric variables (age, gender, height, and weight), as well as health status to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics during cardiopulmonary exercise testing (CPET). These input physiological parameters were derived from 140 laboratory CPET of a diverse cohort of adults (90 males, 50 females; 77 healthy, 63 smokers; average age: 26.6 years), to analyze \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics. The TFN model demonstrated high predictive accuracy to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics, with a Root Mean Square Error (RMSE) of 0.03 L/min and an R-squared ( R 2 ) value of 0.92, indicating robust performance across varied population groups. This TFN model paves the way for practical and cost-effective approach to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) during exercise, with potential integration with consumer health devices to expand accessibility and, enhance its utility for clinical and fitness applications.